Commvault Systems, NetApp, Tuya, GoPro, Alarm.com, Backblaze, and Shengfeng Development are the seven Cloud Storage stocks to watch today, according to MarketBeat's stock screener tool. Cloud storage stocks refer to publicly traded companies that provide services for storing data on remote servers

Top Cloud Storage Stocks Worth Watching - February 07th

NetApp, Commvault Systems, and Alarm.com are the three Cloud Storage stocks to watch today, according to MarketBeat's stock screener tool. Cloud storage stocks refer to publicly traded companies that provide products and services related to storing and managing data in remote servers accessed over

Best Cloud Storage Stocks To Consider - February 03rd

Integration aims to provide AI infrastructure that meets high security standards

Google Distributed Cloud integrates NetApp data storage and intelligence services - DCD

NetApp and Google Cloud expand their partnership to boost AI innovation with advanced, compliant data storage solutions aimed at regulated sectors in Australia.

NetApp & Google Cloud expand collaboration for secure AI

Shares of <strong>NetApp</strong> Inc. shed 1.01% to $84.60 Wednesday, on what proved to be an all-around favorable trading session for the stock market, with the S&amp;P 500...

NetApp Inc. stock outperforms competitors despite losses on the day - MarketWatch

<strong>NetApp</strong> a company that provides on-premises data storage systems and public cloud services, is poised to report its Q3 FY’24 results toward the end of February.

Up 27% Over The Past Year, Will Higher Margins And Cloud Sales Drive NetApp Stock Higher Post Q3 Earnings?

Nova Scotia is introducing a mobile app aimed at giving people easier access to some health services through their phones. Developed at a cost of $10 million, officials say YourHealthNS is a one-stop shop to book services,

New app aimed at helping Nova Scotians access health services and information

To help customers make a no-regrets move to a NetApp block storage solution, a variety of services are now available at no additional ... You expressly agree that your use of the information within this article is at your sole risk. To the fullest extent ...

NetApp extends its storage leadership and innovation at INSIGHT 2023

“Security threats, sustainability goals, and adoption of deep technologies like AI make IT more complex than ever. Holistic solutions that address these challenges and enable new technologies to thrive across hybrid IT environments, is the need of the hour,” Puneet Gupta, managing director,vice president, NetApp India and SAARC, explained.

Experts believe there is an urgent need for unified data storage

NetApp

netapp

Open source Apache Kafka has long been the backbone of real-time data streaming, but it’s traditionally come with a trade-off: keep expanding expensive broker storage or sacrifice historical data retention. With Kafka’s Tiered Storage, it’s a dilemma that’s finally fading (if you know what you’re doing). By offloading older data to cheaper cloud object storage while keeping recent data local for speed, tiered Storage transforms Kafka’s storage economics and unlocks new possibilities for developers. But how does this work in practice, and what challenges should teams realistically expect when implementing it? I recently spoke with Anil Inamdar from NetApp Instaclustr. Anil's an expert in how to use 100% open source data technologies for mission-critical applications, and has a lot of experience with Kafka deployments. We covered everything from cost savings to unexpected uses cases that Tiered Storage makes possible. Here’s what he had to say. Anil Inamdar NetApp Instaclustr Apache Kafka’s Tiered Storage has been gaining attention in the dev community. Could you explain the concept and how it changes Kafka’s traditional storage approach? Traditionally, Kafka deployments can give you a tough choice—either keep expanding your broker storage to hold onto data longer (but with higher storage costs) or accept shorter retention periods and lose that historical data. It’s a classic trade-off that’s been part of Kafka since day one. Kafka Kafka’s Tiered Storage completely flips this model. Instead of keeping everything on expensive local disks, Kafka now separates your data into two distinct tiers. Your recent, hot data stays local for optimal performance, while historical data automatically flows to much cheaper cloud object storage like S3. If you’ve ever wrestled with retention of messages in Kafka, this can be a game-changer. Kafka’s Tiered Storage What’s great about this architecture is that it works like a write-through cache system. Data follows a predictable path: it lands on local storage first, then once segments are closed, they’re asynchronously copied to remote storage. The beauty is that consumers don’t even need to know where the data is coming from; whether they’re reading from local or remote storage happens transparently. Tiered Storage also opens up new use cases. Now you can keep months or even years of data accessible without breaking the bank (so particularly for organizations that need to analyze historical patterns or reprocess data from the past, this is a big deal). The cost savings can be dramatic, especially at scale, since cloud object storage is a fraction of the price of high-performance SSDs. What’s particularly smart about how Tiered Storage was implemented is that it preserves all of Kafka’s core semantics and APIs. Your producers and consumers keep working exactly as they did before. The infrastructure changes, but your applications don’t have to. What are the key technical and business drivers pushing teams toward Kafka’s Tiered Storage? The biggest driver is simple economics. Organizations need to retain more data without proportionally increasing infrastructure costs. As data volumes explode, the traditional approach of scaling broker storage becomes financially unsustainable. Technical teams are pushing for Tiered Storage to enable longer-term analytics and compliance requirements, while CFOs appreciate decoupled compute and storage costs. The ability to leverage cheap(er) cloud storage while maintaining seamless access to historical data enables new possibilities around reprocessing, machine learning training, and regulatory compliance without sacrificing operational performance for real-time workloads. When implementing Kafka Tiered Storage, what performance trade-offs should engineering teams be prepared for, and how can they mitigate potential bottlenecks? First off, they should be prepared for the performance differences when reading from Tiered Storage versus local disks. Our benchmarks show local storage reads can be 2-3x faster than reading from remote storage like S3. The biggest hit comes with small segment sizes. We’ve seen up to 20x degradation there, so resist the temptation to reduce segment size without thorough testing. To mitigate these challenges, you can increase partition counts for topics that need historical data processing. More partitions mean more concurrent consumers can read the data simultaneously, significantly boosting throughput from remote storage. Also, be strategic about your retention settings, and keep frequently accessed data local while offloading less critical data to remote storage. Remember that Kafka producers aren’t affected since remote copying happens asynchronously, but you’ll still want to budget for about 10% additional cluster CPU and network resources to handle background tiering operations. The ability to “time travel” through data is one of Kafka’s powerful capabilities. How does Tiered Storage expand possibilities for applications that need to reprocess historical data streams? Kafka’s time travel was always limited by storage economics—keeping months of data on local disks just wasn’t all that viable for high-volume streams. Tiered Storage changes the equation. Now you can retain years of historical data affordably, transforming reprocessing scenarios from theoretical to practical. Training new ML models on complete datasets, migrating to new sink systems, or auditing past transactions for compliance all become realistic options. Even more powerful is how this impacts development. Found a bug in your processing logic from months ago? Just replay from that point forward. You can experiment more freely, running parallel processing pipelines against the same historical data for A/B testing. I don’t think it’s a stretch to say that Tiered Storage essentially democratizes time travel for Kafka’s users. Many teams still wrestle with right-sizing their Kafka clusters. What fundamental principles should guide capacity planning when implementing Tiered Storage? With Tiered Storage, capacity planning shifts dramatically from “how much disk do I need?” to a, well, more nuanced calculation. Start by separating your workloads. Identify your producer input rate and your consumer patterns, and then determine what portion of data needs to remain local versus remote. As mentioned, budget for a bit more additional CPU and network overhead to handle the tiering operations. You should focus on right-sizing local retention based on access patterns, not total data volume. The most active data should stay local, with everything else moving to cheaper remote storage. Remember that remote storage read performance depends heavily on partition count, so size your topics appropriately for the level of parallel processing you'll need for historical data access. Beyond use cases like compliance and analytics, what creative applications of Kafka Tiered Storage are you seeing engineers pursue? Some of the most interesting Tiered Storage applications I’ve seen involve time-shifted operations. One organization created a digital twin architecture where they use Kafka as both the real-time control plane and the historical simulation environment. By keeping years of operational data accessible through Tiered Storage, they can run complex what-if scenarios against actual historical conditions instead of synthetic data. I’ve also seen novel disaster recovery patterns emerge. Rather than maintaining hot standbys with duplicated infrastructure, companies are using Tiered Storage as a much cheaper recovery mechanism. When needed, they can rapidly spin up new Kafka clusters and reload just the relevant historical data from remote storage. Another interesting one involves, essentially, business time machines that let teams roll back entire application states to specific points in the past. By combining event sourcing with Tiered Storage, these systems can recreate any past state without the prohibitive costs that previously made such capabilities impractical for all but the most critical systems. As data streaming technologies continue to evolve, what innovations do you anticipate seeing in Kafka’s architecture over the next few years? I think we’re headed toward somewhat of an infrastructure disappearance with Kafka. The next evolution of the open source project won’t necessarily be about adding more features, but more about making the infrastructure fade into the background so developers can focus purely on data and business logic. We’re already seeing this start with Tiered Storage separating storage concerns from processing. The logical next step is more granular, serverless-like compute that dynamically scales with workload demands. Imagine Kafka clusters that automatically expand and contract based on actual throughput needs without manual intervention. I also expect we’ll see Kafka evolve beyond the traditional producer-consumer model toward something more like a universal data fabric. The boundaries between streaming, database, and analytics platforms are blurring. Upcoming Kafka architectures will likely incorporate more database-like capabilities—transactions, advanced querying, and integration with compute frameworks—while maintaining its core streaming identity. I’d also keep an eye on self-optimizing systems. As streaming data volumes continue to grow exponentially, manual tuning becomes impossible. We’ll need Kafka systems that can automatically determine optimal partition counts, retention policies, and resource allocation based on observed access patterns and workload characteristics. The Kafka of tomorrow won’t just be a better message broker—it’ll be the intelligent backbone of truly data-driven organizations.

Apache Kafka’s New Tiered Storage: What Developers Need to Know

Entity Framework 8 – Partial Classes Tricks You Should Know About

የድርጅት መዋቅር 8 - ስለ ከፊል ክፍሎች ማወቅ ያለብዎት ዘዴዎች

Entity Framework 8: trucos sobre clases parciales que debes conocer

Entity Framework 8 - Trucos de clases parciais que debes coñecer

Entity Framework 8 - Trik pou klas pasyèl ou ta dwe konnen

Entity Framework 8 – Részleges osztályok Trükkök, amelyeket tudnia kell

Entity Framework 8 – Մասնակի դասերի հնարքներ, որոնց մասին պետք է իմանաք

Entity Framework 8 – 知っておくべき部分クラスのトリック

Entity Framework 8 – ເຄັດລັບຂອງຫ້ອງຮຽນບາງສ່ວນທີ່ທ່ານຄວນຮູ້ກ່ຽວກັບ

Entity Framework 8 — трюки с частичным классом, о которых вам следует знать

Ентити Фрамеворк 8 – Трикови за делимичне класе које треба да знате

Entity Framework 8 – Bilmeniz Gereken Kısmi Sınıflar Hileleri

ہستی کا فریم ورک 8 - جزوی کلاسز کی ترکیبیں جن کے بارے میں آپ کو معلوم ہونا چاہیے۔

Everything Apache Iceberg-Related Announced This Year

GIBRALTAR, Gibraltar, November 29th, 2024/Chainwire/--Hydration has announced the launch of its decentralized borrowing platform, the Hydration Money Market. The new platform allows users to supply cryptocurrency as collateral, earn interest on their deposits, and borrow various digital assets. Built on the Polkadot blockchain, the platform emphasizes efficiency and innovation in the decentralized finance (DeFi) ecosystem. Hydration introduces on-chain prioritized liquidations, a mechanism designed to minimize losses and prevent exploitation during liquidation events. The platform operates as a fork of the AAVE v3 protocol, offering over-collateralized borrowing capabilities and enabling users to explore advanced strategies, such as leveraging positions and arbitraging interest rates. These features cater to users seeking diverse, risk-adjusted financial strategies within the DeFi space. Hydration’s launch is a step forward in its mission to democratize access to financial tools while ensuring sustainable protocol development. The project’s focus on transparency and user-centric design aligns with the broader goals of fostering a robust, decentralized financial ecosystem. For more information, users can visit hydration.net, app.hydration.net or follow Hydration on X (formerly Twitter). About Hydration Hydration is a blockchain-based platform dedicated to enhancing financial accessibility and innovation through decentralized tools. By leveraging Polkadot’s scalability and interoperability, Hydration aims to empower individuals and institutions with secure, transparent, and efficient solutions for borrowing, lending, and managing digital assets. Contact Valery Hydrator valery@intergalactic.limited This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here GIBRALTAR, Gibraltar, November 29th, 2024/Chainwire/--Hydration has announced the launch of its decentralized borrowing platform, the Hydration Money Market. The new platform allows users to supply cryptocurrency as collateral, earn interest on their deposits, and borrow various digital assets. Built on the Polkadot blockchain, the platform emphasizes efficiency and innovation in the decentralized finance (DeFi) ecosystem. Hydration introduces on-chain prioritized liquidations, a mechanism designed to minimize losses and prevent exploitation during liquidation events. The platform operates as a fork of the AAVE v3 protocol, offering over-collateralized borrowing capabilities and enabling users to explore advanced strategies, such as leveraging positions and arbitraging interest rates. These features cater to users seeking diverse, risk-adjusted financial strategies within the DeFi space. Hydration’s launch is a step forward in its mission to democratize access to financial tools while ensuring sustainable protocol development. The project’s focus on transparency and user-centric design aligns with the broader goals of fostering a robust, decentralized financial ecosystem. For more information, users can visit hydration.net , app.hydration.net or follow Hydration on X (formerly Twitter). hydration.net hydration.net app.hydration.net app.hydration.net X X About Hydration Hydration is a blockchain-based platform dedicated to enhancing financial accessibility and innovation through decentralized tools. By leveraging Polkadot’s scalability and interoperability, Hydration aims to empower individuals and institutions with secure, transparent, and efficient solutions for borrowing, lending, and managing digital assets. Hydration Hydration Contact Valery Hydrator valery@intergalactic.limited This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Hydration Unveils Decentralized Borrowing Platform On Polkadot

Hydration разкрива децентрализирана платформа за заеми на Polkadot

Hydration presenta una plataforma de préstamos descentralizada en Polkadot

Hydration Devwale Platfòm Prete Desantralize Sou Polkadot

Hydration-ը Պոլկադոտում ներկայացնում է ապակենտրոնացված փոխառությունների հարթակ

HydrationがPolkadot上で分散型借入プラットフォームを発表

Hydration បង្ហាញវេទិកាកម្ចីវិមជ្ឈការនៅលើ Polkadot

Hydration przedstawia zdecentralizowaną platformę pożyczkową na Polkadot

Hydration revela plataforma de empréstimo descentralizada na Polkadot

Hydration dezvăluie o platformă descentralizată de împrumut pe Polkadot

Hydration Yerekana Ihuriro ryinguzanyo zegerejwe abaturage kuri Polkadot

Biyo-baxa Oo Daaha Ka Qaaday Qaabka Amaahda Baahsan ee Polkadot

ሃይድሬሽን ኣብ ፖልካዶት ዘይተማእከለ ናይ ልቓሕ መድረኽ ኣቃሊዑ

The Ultimate Directory of Apache Iceberg Resources

The 10 Most Powerful AI Image Generators with 4 Sample Images Each

Apache Cassandra—the immensely popular open-source, distributed NoSQL database management system—is approaching the general availability of its 5.0 version (the 5.0 beta is already available). Whether you’re an existing Cassandra user or considering a migration to the open-source database with the release of this new version, there’s a lot to be excited about with 5.0. I spoke with Mo Ansari, a Product Manager at Instaclustr by NetApp, about what developers, engineers, DBAs, and other users should know about Cassandra 5.0—from database upgrades to new use cases to open-source community support. Mo knows Cassandra particularly well (Instaclustr has long had a managed platform around the fully open-source version of the database, which Mo works on). Here’s what Mo had to say about Cassandra 5.0: Cassandra 5.0 promises improved database performance and scalability—what are some specific optimizations or architectural changes that have been made to achieve this? Cassandra 5.0 adds in the advantages of trie (prefix tree)-based Memtables and SSTables—meaning the new version of the open source NoSQL database offers significant potential for improved database performance and memory optimization. Cassandra’s performance for reads and modification operations (and its ability to correctly size structures to data) benefit from these storage formats, which make use of tries and byte-comparable representations of database keys. Memory management overhead and garbage collection also face fewer burdens with trie Memtables and trie-indexed SSTables, delivering even more benefits for Cassandra users (especially those using the database at scale). Aside from performance, are there other changes with Cassandra 5.0 that developers should be excited about? Developers will want to get their hands on Cassandra 5.0’s vector support, including Vector Search for locating content in large datasets. They can also utilize new CQL vector functions and a new vector data type designed for saving and retrieving embeddings vectors. Because of these improvements, Cassandra 5.0 is well-positioned as a data layer technology for supporting AI/ML application development. ML models function by comparing similarities among data and putting data connections in context. Embeddings vectors power similarity comparisons by offering arrays of floating-point numbers representing how similar particular objects are to each other. With Cassandra 5.0, developers now have a powerful (and open source) database with that specific functionality that’s so important to AI/ML applications. Developers should also be hyped about storage-attached indexing, which makes secondary indexes far more usable and efficient. On a Cassandra database table, developers can now easily create one or more secondary indexes, each based on a single column they choose. The result is massively scalable and globally distributed indexing with search throughput that can’t be beat (useful with Vector Search), modular extensibility (also demonstrated by Vector Search), and stunning indexing functionality that captures semantics via queries and content (including large documents and images). Last but not least, developers using Cassandra 5.0 will like the ability to build their own user-defined functions, and to utilize the range of useful new native CQL aggregation and math functions available with the new version. How has the developer experience changed—if it has—with Cassandra 5.0? Cassandra is bringing significant enhancements to the developer experience. The new release features open the door for a vast number of use cases and balance the developer experience. The new version focuses on ease of use, performance, and security and includes features such as storage-attached indexes that make queries on non-primary key columns more efficient. This reduces the complexity and overhead associated with secondary indexes. Additionally, the new version is anticipated to support ACID transactions with version 5.1, which will bring SQL-like functionality to Cassandra—making it more approachable for developers familiar with relational databases. Furthermore, the new version has more guardrails and enhanced tooling, including a new virtual table to view system lows, which will aid in the development process. Overall, Cassandra 5.0 is packed with features that will empower developers to work faster, create more efficient queries, and manage data with increased security and control! Do you anticipate the use cases for Cassandra to change or evolve based on what’s included in 5.0? Yes, with a new vector datatype and storage-attached indexes improving the performance of the usage of vectors, Apache Cassandra is positioning itself as a contender in the AI market. Over the years, Cassandra’s exceptional write throughput and ability to handle large volumes of data made it ideal for several use cases and applications, such as scalable web applications, messaging systems, and event logging and monitoring systems. However, Cassandra 5.0 is going to significantly alter the landscape. Apart from all the use cases that Cassandra supported and suited in the past, version 5.0 will extend its applicability to more complex, transactional, and analytical applications, bridging the gap between NoSQL flexibility and the rigorous demands of modern data-intensive applications. I see several new use cases, such as analytics and machine learning, niche financial services requiring ACID transactions, applications requiring complex querying capabilities, etc. As the Apache Cassandra project calls it, “moving towards an AI-driven future.” With increasing adoption of cloud and containerized environments, how has Cassandra 5.0 been designed to support these deployment models? Apache Cassandra has always been designed with distributed and fault-tolerant computing and scaling in mind. It has always been a good choice for cloud-based workloads. While not introducing specific features under the “cloud-native” label, Cassandra 5.0 continues to support and enhance deployments in cloud and containerized environments through its scalable, resilient, and community-driven projects. Features included in the 4.1 version release have already paved the way for a more cloud-native future for Cassandra. The K8ssandra project is another great example of a commitment to a cloud-native future. With any new release, there’s always a learning curve. How do you see the adoption process going for Cassandra 5.0, and what advice would you give to developers who are looking to migrate? The transition to Cassandra 5.0 does bring a learning curve, but it’s designed to be as smooth as possible. I anticipate gradual adoption as developers familiarize themselves with new features and improvements. I expect the adoption will be pretty quick in dev and testing environments; however, it will be gradual in production environments. Given the focus on cost reduction and AI readiness, there are a lot more reasons, use cases, motivations, and other drivers for adoption. Based on its several utilities, I'm guessing that Apache Cassandra will become the preferred choice for AI workloads. For those looking to migrate, my advice is to leverage the extensive resources available on the Apache website, including its documentation and community forums. Planet Cassandra is a fantastic place to look at the use cases. I would also advise the developers to engage in the community for shared insights and best practices through mailing lists, project town halls, and other events such as contributor meetings. Cassandra has a large and active open-source community. How has the community been involved in the development of 5.0? Apache Cassandra has a large and vibrant open-source community, playing a crucial role in the development of 5.0. Members have been actively involved in proposing new features and improving existing ones based on real-world use cases and future needs. Feedback was collected through various channels, such as mailing lists, Jira tickets, and Cassandra Enhancement Proposals (CEPs), and it has been instrumental in shaping the release to meet the evolving needs of users. The community has been actively contributing to many more features that are currently in the draft stage and will bring many more benefits to Cassandra in the future. Apache Cassandra—the immensely popular open-source, distributed NoSQL database management system—is approaching the general availability of its 5.0 version (the 5.0 beta is already available). 5.0 beta Whether you’re an existing Cassandra user or considering a migration to the open-source database with the release of this new version, there’s a lot to be excited about with 5.0. I spoke with Mo Ansari, a Product Manager at Instaclustr by NetApp , about what developers, engineers, DBAs, and other users should know about Cassandra 5.0—from database upgrades to new use cases to open-source community support. Instaclustr by NetApp Mo knows Cassandra particularly well (Instaclustr has long had a managed platform around the fully open-source version of the database, which Mo works on). Here’s what Mo had to say about Cassandra 5.0: Cassandra 5.0 promises improved database performance and scalability—what are some specific optimizations or architectural changes that have been made to achieve this? Cassandra 5.0 adds in the advantages of trie (prefix tree)-based Memtables and SSTables—meaning the new version of the open source NoSQL database offers significant potential for improved database performance and memory optimization. Cassandra’s performance for reads and modification operations (and its ability to correctly size structures to data) benefit from these storage formats, which make use of tries and byte-comparable representations of database keys. Memory management overhead and garbage collection also face fewer burdens with trie Memtables and trie-indexed SSTables, delivering even more benefits for Cassandra users (especially those using the database at scale). Aside from performance, are there other changes with Cassandra 5.0 that developers should be excited about? Developers will want to get their hands on Cassandra 5.0’s vector support, including Vector Search for locating content in large datasets. They can also utilize new CQL vector functions and a new vector data type designed for saving and retrieving embeddings vectors. Because of these improvements, Cassandra 5.0 is well-positioned as a data layer technology for supporting AI/ML application development. ML models function by comparing similarities among data and putting data connections in context. Embeddings vectors power similarity comparisons by offering arrays of floating-point numbers representing how similar particular objects are to each other. With Cassandra 5.0, developers now have a powerful (and open source) database with that specific functionality that’s so important to AI/ML applications. Developers should also be hyped about storage-attached indexing, which makes secondary indexes far more usable and efficient. On a Cassandra database table, developers can now easily create one or more secondary indexes, each based on a single column they choose. The result is massively scalable and globally distributed indexing with search throughput that can’t be beat (useful with Vector Search), modular extensibility (also demonstrated by Vector Search), and stunning indexing functionality that captures semantics via queries and content (including large documents and images). Last but not least, developers using Cassandra 5.0 will like the ability to build their own user-defined functions, and to utilize the range of useful new native CQL aggregation and math functions available with the new version. How has the developer experience changed—if it has—with Cassandra 5.0? Cassandra is bringing significant enhancements to the developer experience. The new release features open the door for a vast number of use cases and balance the developer experience. The new version focuses on ease of use, performance, and security and includes features such as storage-attached indexes that make queries on non-primary key columns more efficient. This reduces the complexity and overhead associated with secondary indexes. Additionally, the new version is anticipated to support ACID transactions with version 5.1, which will bring SQL-like functionality to Cassandra—making it more approachable for developers familiar with relational databases. Furthermore, the new version has more guardrails and enhanced tooling, including a new virtual table to view system lows, which will aid in the development process. Overall, Cassandra 5.0 is packed with features that will empower developers to work faster, create more efficient queries, and manage data with increased security and control! Do you anticipate the use cases for Cassandra to change or evolve based on what’s included in 5.0? Yes, with a new vector datatype and storage-attached indexes improving the performance of the usage of vectors, Apache Cassandra is positioning itself as a contender in the AI market. Over the years, Cassandra’s exceptional write throughput and ability to handle large volumes of data made it ideal for several use cases and applications, such as scalable web applications, messaging systems, and event logging and monitoring systems. However, Cassandra 5.0 is going to significantly alter the landscape. Apart from all the use cases that Cassandra supported and suited in the past, version 5.0 will extend its applicability to more complex, transactional, and analytical applications, bridging the gap between NoSQL flexibility and the rigorous demands of modern data-intensive applications. I see several new use cases, such as analytics and machine learning, niche financial services requiring ACID transactions, applications requiring complex querying capabilities, etc. As the Apache Cassandra project calls it, “ moving towards an AI-driven future .” moving towards an AI-driven future With increasing adoption of cloud and containerized environments, how has Cassandra 5.0 been designed to support these deployment models? Apache Cassandra has always been designed with distributed and fault-tolerant computing and scaling in mind. It has always been a good choice for cloud-based workloads. While not introducing specific features under the “cloud-native” label, Cassandra 5.0 continues to support and enhance deployments in cloud and containerized environments through its scalable, resilient, and community-driven projects. Features included in the 4.1 version release have already paved the way for a more cloud-native future for Cassandra. The K8ssandra project is another great example of a commitment to a cloud-native future. 4.1 version release K8ssandra project With any new release, there’s always a learning curve. How do you see the adoption process going for Cassandra 5.0, and what advice would you give to developers who are looking to migrate? The transition to Cassandra 5.0 does bring a learning curve, but it’s designed to be as smooth as possible. I anticipate gradual adoption as developers familiarize themselves with new features and improvements. I expect the adoption will be pretty quick in dev and testing environments; however, it will be gradual in production environments. Given the focus on cost reduction and AI readiness, there are a lot more reasons, use cases, motivations, and other drivers for adoption. Based on its several utilities, I'm guessing that Apache Cassandra will become the preferred choice for AI workloads. For those looking to migrate, my advice is to leverage the extensive resources available on the Apache website , including its documentation and community forums. Planet Cassandra is a fantastic place to look at the use cases. I would also advise the developers to engage in the community for shared insights and best practices through mailing lists, project town halls, and other events such as contributor meetings. Apache website Planet Cassandra Cassandra has a large and active open-source community. How has the community been involved in the development of 5.0? Apache Cassandra has a large and vibrant open-source community , playing a crucial role in the development of 5.0. Members have been actively involved in proposing new features and improving existing ones based on real-world use cases and future needs. Feedback was collected through various channels, such as mailing lists, Jira tickets, and Cassandra Enhancement Proposals (CEPs), and it has been instrumental in shaping the release to meet the evolving needs of users. open-source community The community has been actively contributing to m any more features that are currently in the draft stage and will bring many more benefits to Cassandra in the future. any more features

Here's What to Know About Apache Cassandra 5.0

With the explosive growth of ChatGPT, discussions around how to integrate AI have made their way into corporate boardrooms and product roadmaps at a rapid pace. In fact, AI already plays a vital role in our daily lives. Billions of people spend hours every day glued to apps such as Instagram, TikTok, Facebook, and Twitter, all of which use AI ranking algorithms to personalize your feed. These algorithms analyze thousands of data points per second to predict what a user would like to see most. And the key to getting personalization right is user data. Except, when dealing with user data, it is critical to uphold user privacy laws and protect users from breaches. The mainstream public is still shaken by how used people’s personal data to maneuver politics. Cambridge Analytica According to a report by the Identity Theft Resource Center (ITRC), the US experienced a record number of 1,862 data breaches in 2021. Prominent companies such as Microsoft, LinkedIn, Facebook, Plex, and Cash App experienced significant in just the last two years. data breaches Fortunately, product experts like Sunil Tej Gorantla, who are passionate about building products, are focusing on keeping user data sacrosanct. Having graduated from VIT University, one of the top engineering institutions in India, with a bachelor’s degree in Information Technology, Sunil has a technical background. With more than a decade of experience in product management, Sunil has helped various startups build large-scale web and mobile applications that generate millions in revenue. He is a founder, product expert, growth hacker, and writer who believes in solving what people need, making data-driven decisions, and building products with a strong mission at their core to serve others. During his extensive career, Sunil was fortunate to be mentored by some of the industry's eminent personalities, such as Harishankaran Karunanidhi, Hunter Walk, and Justin Kan. Sunil spent the early years of his career working with leading high-growth early-stage startups. He joined HackerRank as the first Product Evangelist immediately after the company became . To acquire HackerRank's first few thousand users, he spearheaded the campus ambassador program. India’s first YCombinator startup He recruited hundreds of student ambassadors who formed Hacker Clubs at their universities to encourage students to solve programming challenges and participate in weekly programming contests online. He also collaborated with companies such as Airbnb, Adobe, Quora, and NetApp, which hired top programming talent from these contests by being sponsors. However, at the time, retaining users after the contests was a challenge for HackerRank. To improve retention, Sunil implemented gamification and designed HackerRank’s first incentive mechanism to keep users hooked to the product. Working with the team, he launched "Hackos"—a term he coined—which are points users earned by solving programming challenges. Users loved this feature so much that the product's retention rate experienced a significant increase. Sunil's experience scaling HackerRank from scratch to the first 200k users has made his skillset attractive to startups worldwide. His ability to articulate unmet user needs and build product features to drive growth led him to places he least expected. As a Growth Hacker, he helped scale Mobilizr, a consumer tech startup in Nigeria. He also worked as Head of Data for a property tech startup in London. Afterward, he joined Global Savings Group (GSG) in Munich, Germany, as the first Product Owner for CupoNation's mobile web application. In just a few months, he launched CupoNation's first mobile web application in 17+ countries, which tripled the revenue generated from mobile users. Today, GSG is one of the most successful ventures from Rocket Internet, which also spun off Delivery Hero, Zalando, HelloFresh, and Jumia. After leaving GSG, Sunil joined Juniqe, a startup that was ranked as one of the top 3 fastest growing companies in Germany at the time. As an experienced Product Manager, he managed two cross-functional teams to rebuild the entire technical stack, scaling the mobile and web applications to handle millions of users. In addition to leading the Conversion Rate Optimization (CRO) team, he was also the stakeholder manager for Marketing, Search Engine Optimization (SEO), and Customer Relationship Management (CRM). During his time at Juniqe, he played a pivotal role in scaling the product from less than a million to €20 million in annual recurring revenue, turning the startup into one of the largest art e-commerce sites in Europe. While exploring strategies to improve conversion rates at Juniqe, Sunil encountered Dynamic Yield (DY), a personalization solution for e-commerce products. His foray into personalization began as he worked on integrating DY with Juniqe to bring features like “Users who bought this also liked” and “Products you might like.” As one of the first handful of customers to try out DY, he worked closely with DY's team to fix their product issues and share feedback to improve their machine learning (ML) algorithms. This gave Sunil a behind-the-scenes look into how ML was deployed at scale and equipped him to suggest strategies that helped DY become successful. Gartner has named DY a leader in the Magic Quadrant for Personalization Engines for the past five consecutive years. Later, DY was first acquired by McDonald's and then by Mastercard to enhance customer experience using personalization. Armed with the experience transforming user experiences using personalization, Sunil currently leverages his expertise in product development and growth hacking to build Gulpie—a personalized food discovery application. The app helps people discover restaurants based on their unique tastes, while also helping restaurants fight against the manipulation of their reviews and empowering reviewers to monetize their expertise. To create Gulpie, Sunil interviewed hundreds of food lovers, food reviewers, and restaurant owners. Through these interviews, he discovered that a lack of trust in restaurant reviews and user data protection leads to inefficiencies in the current restaurant discovery process, leaving many people perplexed and frustrated. To address inefficiencies in the market, Sunil built a state-of-the-art personalized recommendation engine using an ensemble of cutting-edge machine-learning algorithms. This engine provides users with personalized restaurant recommendations based on their individual tastes, helping them make informed decisions without going through a cumbersome search process. While working on the recommendation engine, Sunil gained key insights into how to deduce unique user tastes. He found that gaining users' trust to share their data requires a promise of user data protection and strict privacy measures. Leaders in the industry like Sriram Krishnan—an investor at a16z who previously built product teams at Twitter, Meta, and Snap—share this sentiment. https://twitter.com/sriramk/status/1631779635376828416?s=46&t=m-tchH2RTFFMPB2qJSBrFQ What Sunil Had to Say Sharing his experience working on personalization at Gulpie, Sunil explains: “Before the invention of blockchains, users were unable to truly own their data as centralized entities held control over it. However, with Gulpie, our aim is to empower users to take control of their data and provide them with better visibility into its usage. We understand that users expect product builders to be cognizant of the skepticism people experience when sharing their data with apps. That’s why we focus on enabling personalization while ensuring that users’ data is absolutely not at risk. We anonymize user data before training our machine-learning models. Additionally, with GDPR and other privacy protection regulations in place, it's important for companies to actively monitor and prevent the misuse of user data to avoid situations like the Cambridge Analytica-Facebook scandal.” In an age where large organizations scavenge user data and abuse privacy without consent, product experts like Sunil lead the way in protecting user privacy. As product managers, builders, and founders strive for personalization without sacrificing privacy, they can rely on experts like Sunil who push the boundaries of what’s possible. With his expertise and vision, we can look forward to a future where user privacy is truly protected, and products can provide tailored experiences that put users first.

Expert Product Manager Sunil Tej Gorantla on Personalization Without User Data at Risk

Oracle vs. Google (2014) Court Filing, retrieved on May 9, 2014, is part of . You can jump to any part in this filing . This is part 1 of 16. HackerNoon’s Legal PDF Series here Introduction E. JOSHUA ROSENKRANZ, Orrick, Herrington & Sutcliffe LLP, of New York, New York, argued for plaintiffappellant. With him on the brief were MARK S. DAVIES, ANDREW D. SILVERMAN, KELLY M. DALEY; and ANNETTE L. HURST, GABRIEL M. RAMSEY, and ELIZABETH C. MCBRIDE, of San Francisco, California. Of counsel on the brief were DORIAN E. DALEY, DEBORAH K. MILLER, MATTHEW SARBORARIA, and ANDREW C. TEMKIN, Oracle America, Inc., of Redwood Shores, California; and DALE M. CENDALI, DIANA M. TORRES, SEAN B. FERNANDES, and JOSHUA L. SIMMONS, Kirkland & Ellis LLP, of New York, New York. Of counsel were SUSAN M. DAVIES, Kirkland & Ellis LLP, of New York, New York; MICHAEL A. JACOBS, Morrison & Foerster LLP, of San Francisco, California; and KENNETH A. KUWAYTI, of Palo Alto, California. ROBERT A. VAN NEST, Keker & Van Nest LLP, of San Francisco, California, argued for defendant-crossappellant. With him on the brief were CHRISTA M. ANDERSON, STEVEN A. HIRSCH, MICHAEL S. KWUN, and DANIEL E. JACKSON. Of counsel on the brief were IAN C. BALLON and HEATHER MEEKER, Greenberg Traurig, LLP, of East Palo Alto, California; RENNY HWANG, Google Inc., of Mountain View, California; and DARYL L. JOSEFFER and BRUCE W. BABER, King & Spalding LLP, of Washington, DC. MARCIA B. PAUL, Davis Wright Tremaine LLP, of New York, New York, for amicus curiae Ralph Oman. With her on the brief were LACY H. KOONCE, III and DEBORAH A. ADLER. WILLIAM A. RUDY, Lathrop & Gage LLP, of Kansas City, Missouri, for amici curiae Picture Archive Council of America, Inc., et al. With him on the brief were CAROLE E. HANDLER and BRIANNA E. DAHLBERG, of Los Angeles, California. GREGORY G. GARRE, Latham & Watkins, LLP, of Washington, DC, for amici curiae Microsoft Corporation, et al. With him on the brief was LORI ALVINO MCGILL. Of counsel on the brief were PAUL T. DACIER, KRISHNENDU GUPTA, EMC Corporation, of Hopkinton, Massachusetts; and DOUGLAS LUFTMAN, NETAPP, Inc., of Sunnyvale, California. JARED BOBROW, Weil, Gotshal & Manges LLP, of Redwood Shores, California, for amici curiae Eugene H. Spafford, Ph.D., et al. With him on the brief was AARON Y. HUANG. MATTHEW S. HELLMAN, Jenner & Block LLP, of Washington, DC, for amicus curiae BSA/The Software Alliance. With him on the brief was PAUL M. SMITH. STEVEN T. COTTREAU, Clifford Chance US LLP, of Washington, DC, for amici curiae, Scott McNealy, et al. MEREDITH JACOB, Program on Information Justice and Intellectual Property, American University, Washington College of Law, of Washington, DC, for amici curiae Intellectual Property Law Professors. JULIE P. SAMUELS, Electronic Frontier Foundation, of San Francisco, California, for amici curiae Computer Scientists. With her on the brief was MICHAEL BARCLAY. Of counsel on the brief was JASON M. SCHULTZ, NYU Technology Law and Policy Clinic, NYU School of Law, of New York, New York. JONATHAN BAND, Jonathan Band PLLC, of Washington, DC, filed a brief for amicus curiae Computer & Communications Industry Association. With him on the brief was MATTHEW SCHRUERS, Computer & Communications Industry Association, of Washington, DC. CHAD RUBACK, The Ruback Law Firm, of Dallas, Texas, filed a brief for amici curiae Rackspace US, Inc., et al. JENNIFER M. URBAN, Samuelson Law, Technology and Public Policy Clinic, U.C. Berkeley School of Law, of Berkeley, California for amici curiae Software Innovators, et al. Before O’MALLEY, PLAGER, and TARANTO, Circuit Judges. O’MALLEY, Circuit Judge. This copyright dispute involves 37 packages of computer source code. The parties have often referred to these groups of computer programs, individually or collectively, as “application programming interfaces,” or API packages, but it is their content, not their name, that matters. The predecessor of Oracle America, Inc. (“Oracle”) wrote these and other API packages in the Java programming language, and Oracle licenses them on various terms for others to use. Many software developers use the Java language, as well as Oracle’s API packages, to write applications (commonly referred to as “apps”) for desktop and laptop computers, tablets, smartphones, and other devices. Oracle filed suit against Google Inc. (“Google”) in the United States District Court for the Northern District of California, alleging that Google’s Android mobile operating system infringed Oracle’s patents and copyrights. The jury found no patent infringement, and the patent claims are not at issue in this appeal. As to the copyright claims, the parties agreed that the jury would decide infringement, fair use, and whether any copying was de minimis, while the district judge would decide copyrightability and Google’s equitable defenses. The jury found that Google infringed Oracle’s copyrights in the 37 Java packages and a specific computer routine called “rangeCheck,” but returned a noninfringement verdict as to eight decompiled security files. The jury deadlocked on Google’s fair use defense. After the jury verdict, the district court denied Oracle’s motion for judgment as a matter of law (“JMOL”) regarding fair use as well as Google’s motion for JMOL with respect to the rangeCheck files. Order on Motions for Judgment as a Matter of Law, Oracle Am., Inc. v. Google Inc., No. 3:10-cv-3561 (N.D. Cal. May 10, 2012), ECF No. 1119. Oracle also moved for JMOL of infringe-ment with respect to the eight decompiled security files. In granting that motion, the court found that: (1) Google admitted to copying the eight files; and (2) no reasonable jury could find that the copying was de minimis. Oracle Am., Inc. v. Google Inc., No. C 10-3561, 2012 U.S. Dist. LEXIS 66417 (N.D. Cal. May 11, 2012) (“Order Granting JMOL on Decompiled Files”). Shortly thereafter, the district court issued its decision on copyrightability, finding that the replicated elements of the 37 API packages—including the declaring code and the structure, sequence, and organization—were not subject to copyright protection. Oracle Am., Inc. v. Google Inc., 872 F. Supp. 2d 974 (N.D. Cal. 2012) (“Copyrightability Decision”). Accordingly, the district court entered final judgment in favor of Google on Oracle’s copyright infringement claims, except with respect to the rangeCheck code and the eight decompiled files. Final Judgment, Oracle Am., Inc. v. Google Inc., No. 3:10-cv3561 (N.D. Cal. June 20, 2012), ECF No. 1211. Oracle appeals from the portion of the final judgment entered against it, and Google cross-appeals from the portion of that same judgment entered in favor of Oracle as to the rangeCheck code and eight decompiled files. Because we conclude that the declaring code and the structure, sequence, and organization of the API packages are entitled to copyright protection, we reverse the district court’s copyrightability determination with instructions to reinstate the jury’s infringement finding as to the 37 Java packages. Because the jury deadlocked on fair use, we remand for further consideration of Google’s fair use defense in light of this decision. With respect to Google’s cross-appeal, we affirm the district court’s decisions: (1) granting Oracle’s motion for JMOL as to the eight decompiled Java files that Google copied into Android; and (2) denying Google’s motion for JMOL with respect to the rangeCheck function. Accordingly, we affirm-in-part, reverse-in-part, and remand for further proceedings. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 10-CV-3561 retrieved on September 22, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. law.justia.com

Google Admits to Copying Eight Decompiled Security Files Developed by Oracle as Part of API Package

Enterprise data solutions can quickly become expensive. reports that organizations see 30% data growth every 12 months and risk seeing their solutions fail if this growth isn’t managed. NetApp echoes the concerns, stating that data solutions costs tend to be an afterthought, not addressed until they’re already a problem. Gartner Being a data-driven organization, you already know this and understand the critical need for cost optimization in your data solutions. So what can you do? In this article, I’ll look at the factors that increase costs, what you can do to contain them, and how stream processing can be a major source of savings. Through it all, I’ll help you create an effective plan that leads to operational efficiencies in data solutions. Why Are Data Solutions Expensive? First, let’s look at the factors that contribute to the increasing costs of data solutions. You probably already face many, if not all, of these situations: : Data is being generated at an accelerated rate. And the cost of storing and managing that data has also increased. Increasing volume of data : Disparate data sources make data integration a herculean task that leads to increased data quality checks and management costs. A myriad of data sources and their associated complexity : Inevitable technological advancements lead to frequent upgrades in hardware and software, resulting in higher costs. Frequently updated technology : Not only do we have to worry about how to store and manage data, but also how to protect it from bad actors. Data privacy is critical, demanding hefty investments in encryption, security, and maintenance. Data security and privacy concerns : Many organizations hoard a tremendous amount of data before consulting their data practitioners and experts. This leads to unnecessary storage costs, incompatible systems, and inefficient and unscalable solutions. Data consolidation : According to , the big data market will grow to $229.4 billion by 2025. With that growth comes the need for specialized—and expensive—skills, such as Apache Kafka, Apache Flink, Hadoop, Docker, containerization, DevOps, and others. Lack of skilled data professionals Bloomberg How Can Enterprises Reduce Their Data Solution Costs? What can enterprises do to keep these costs down? There are many strategies, including: Data compression and archiving (reduce data size and archive unused data) Data partitioning (make it easier and faster to process data) Data caching (keep frequently used data in high-speed storage) On-demand autoscaling Data governance measures (ensure accurate, complete, and well-managed data) Efficient data movement (improve routing from cloud and on-prem in order to speed insights) Cloud-agnostic deployment (reduce vendor lock-in, optimize costs) Using a multi-cloud approach Let’s add one more to this list, the solution that we’ll look at in more detail in this article: . stream processing What Is Stream Processing? Stream processing, as the name suggests, is a data management strategy that involves ingesting (and acting on) continuous data streams—such as the user’s clickstream journey, sensor data, or sentiment from a social media feed—in real time. By using stream processing, organizations can and see an over common frameworks for managing data, such as Splunk, Apache Spark, and others. greatly reduce their data solutions costs increased ROI Stream processing solves many of the issues encountered with other data solutions, including rising costs, by giving you: : You handle the data in real time. Reduced storage costs : Stream processing systems can handle large-scale data using a distributed computing architecture. Real-time scalability : Stream processing enables real-time data validation, transformation, and filtering at the source. Preventing the downstream flow of bad data : Real-time analysis gives you real-time analytics—and real-time insights. Faster decision-making to identify high-impact opportunities : Automated workflows in stream processing platforms reduce complexity by abstracting the low-level details of data processing. This enables your data team to focus on data analytics, visualization, and reporting. Team efficiency through automation Manage Stream Processing Effectively Stream processing is powerful and opens many use cases, but managing the implementation is no easy task! Let’s look at some challenges associated with implementing stream processing from scratch. Storage and Computing Resources Anomalies in streaming data can be difficult and time-consuming to detect and address without specialized tools. Continuous Delivery Deploying updates to streaming applications can be challenging without disrupting the production version of the application. Database Integration Integrating data from different databases can be complex and time-consuming for developers. Handling schema management and scalability frequently becomes cumbersome for developers when integrating databases. Migrating data from one database to another can be a costly and time-consuming exercise, requiring additional infrastructure. Developer Productivity Developing streaming applications requires your developer to invest a significant amount of time in managing the infrastructure and configuration. Such efforts increase with the complex data processing logic, taking more time away from actual application development. We can solve these issues by using a stream processing framework, such as . A stream processing framework connects to an upstream resource to stream data in real-time, receive the data, process it, then send it on to a destination. Meroxa’s Turbine Meroxa has a on what this looks like at a high level. If we were to look more closely at how we might use Go to interact with Turbine, we could look at : good write-up code from this example application func (a App) Run(v turbine.Turbine) error {
	source, err := v.Resources("demopg")
	if err != nil {
		return err
	}
	// a collection of records, which can't be inspected directly
	records, err := source.Records("user_activity", nil)
	if err != nil {
		return err
	}
	// second return is dead-letter queue
	result := v.Process(records, Anonymize{})

	dest, err := v.Resources("s3")
	if err != nil {
		return err
	}
	err = dest.Write(result, "data-app-archive")
	if err != nil {
		return err
	}

	return nil
}

func (f Anonymize) Process(records []turbine.Record) []turbine.Record {
	for i, r := range records {
		hashedEmail := consistentHash(r.Payload.Get("email").(string))
		err := r.Payload.Set("email", hashedEmail)
		if err != nil {
			log.Println("error setting value: ", err)
			break
		}
		records[i] = r
	}
	return records
}

func consistentHash(s string) string {
	h := md5.Sum([]byte(s))
	return hex.EncodeToString(h[:])
} In the above code, we see the following steps in action: Create an upstream source (named ), from a PostgreSQL database (named ). source demopg Fetch records (from the table) from that upstream source. user_activity Call , which performs the stream processing. This process iterates through the list of records and overwrites the email of each record with an encoded hash. v.Process Create a downstream destination (named ), using AWS S3. dest Write the resulting stream-processed records to the destination. As we can see, you only need a little bit of code for Turbine to process new records and stream them to the destination. Using a framework such as Turbine for stream processing brings several benefits, including: It reduces the need for storage and computing resources by using stream processing instead of batch processing. It provides a powerful mechanism to from large volumes of streaming data, assisting with a quick fix using Novelty Detector, a highly specialized real-time anomaly detection tool. identify anomalous behavior Because for streaming applications, Turbine streaming data apps offer Feature Branch Deploys, allowing you to deploy the branch whenever it is ready—without impacting the production version of the application. continuous delivery is a big challenge It integrates any source database to any destination database by leveraging that receives real-time streams and publishes them downstream. The Meroxa platform handles data transformation or processing logic and orchestration through the Turbine app, sparing the developers from worrisome schema management or scalability issues. Change Data Capture (CDC) Ultimately, this allows developers to focus their time and effort on your core business needs. It also reduces the cost of migration associated with additional infrastructure. Its code-first integration is aimed at helping developers to focus on building applications, leading to faster development while also reducing the infrastructure typically needed to support stream processing. Conclusion Data-driven organizations are betting high on data assets to fuel their transformation journeys. But they need better—and more cost-efficient—ways to manage the fast-growing datasets. Stream processing provides the necessary benefits: improved data quality, faster decision-making, and more. But it requires the right plan and the right stream processing platform to handle the data efficiently. By following the tips in this article, you should be well on your way!

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